A Red, Green, Blue plus Depth (RGB-D) camera is a camera capable of generating three-dimensional images (a two-dimensional image in a plane plus a depth diagram image). Conventional RGB-D cameras have two different groups of sensors. One of the groups comprises optical receiving sensors (such as RGB cameras), which are used for receiving ambient images that are conventionally represented with respective strength values of three colors: R (red), G (green) and B (blue). The other group of sensors comprises infrared lasers or structured light sensors for detecting a distance (or depth) (D) of an object being observed and for acquiring a depth diagram image. Applications of RGB-D cameras include spatial imaging, gesture identifications, distance detection, and the like.
One type of RGB-D camera applies an infrared light source for imaging (e.g., the Microsoft Kinect). Such a camera has a light source that can emit infrared light with specific spatial structures. Additionally, such a camera is equipped with a lens and a filter chip for receiving the infrared light. An internal processor of the camera calculates the structures of the received infrared light, and through variations of the light structures, the processor perceives the structure and distance information of the object.
Conventional RGB-D cameras, such as the Microsoft Kinect, utilize an infrared light detection approach for acquiring depth information. However, the approach based on infrared light detection works poorly in outdoor settings, especially for objects illuminated by sunlight because the sunlight spectrum has a strong infrared signature that can conceal the infrared light emitted from a detector. Some infrared light detectors attempt to solve this issue by increasing their power, (e.g., with laser or by increasing the strength of the light source). However, this approach is undesirable because it requires greater power consumption.
Optical flow is a pattern of apparent motion of objects, surfaces and edges in a visual scene caused by the relative motion between a camera and the scene. Conventional optical flow is only able to compare movement relative to a pixel field, and not in terms of real-world distances and velocities. Accordingly, conventional optical-flow systems and methods are not suitable for robust applications in real-world environments including navigation of mobile platforms such as unmanned aerial vehicles (UAVs) or other vehicles.
In view of the foregoing, a need exists for an improved optical-flow imaging system and method to overcome the aforementioned obstacles and deficiencies of conventional optical-flow imaging systems.